You're right, thinking of these concise labels as potential "trigger words" or "directives" for AI natural language programming opens up a fascinating avenue. If an AI model could be trained or fine-tuned to strongly associate specific terms with complex cognitive operations, it could indeed be incredibly powerful and efficient. Let's brainstorm this further, considering how this might work and what kind of labels could be effective: Conceptualizing "AI Natural Language Programming Directives": The idea is to create a vocabulary of terms that, when present in a prompt, reliably activate specific internal "reasoning modules" or processing pathways within the AI. This would be more direct than relying on the AI to infer the desired cognitive task from more verbose natural language. Potential Categories of Directives and Examples: We can expand on the initial categories and think about more granular cognitive functions: 1. Analysis & Decomposition: * Deconstruct: (Break down the following into its constituent parts.) * ElementsOf: (Identify the key elements of the following.) * StructureOf: (Describe the underlying structure of the following.) * MechanicsOf: (Explain how the following works.) * RootCauseOf: (Identify the fundamental cause of the following.) 2. Synthesis & Integration: * Synthesize: (Combine the following information into a coherent whole.) * Connect: (Identify the relationships between the following concepts.) * Integrate: (Merge the following ideas into a unified perspective.) * Generalize: (Formulate a broader principle from the following examples.) * Abstract: (Extract the core essence from the following.) 3. Hypothetical & Counterfactual Thinking: * ImagineIf: (Consider the scenario where...) * Counterfact: (Explore what would have happened if...) * Scenario: (Generate possible future scenarios based on...) * ExploreAlt: (Investigate alternative possibilities for...) 4. Constraint & Goal-Oriented Thinking: * Constraints: (Consider the following limitations when responding...) * Objective: (Focus your response on achieving the following goal...) * OptimizeFor: (Generate a response that maximizes...) * Prioritize: (Rank the following aspects in order of importance...) 5. Epistemic Awareness (Thinking about Knowledge): * EpistemicStatus: (Evaluate the certainty and reliability of the following information.) * EvidenceFor: (What evidence supports the following claim?) * EvidenceAgainst: (What evidence contradicts the following claim?) * KnowledgeGap: (Identify what information is missing regarding...) 6. Creative & Divergent Thinking: * Brainstorm: (Generate a range of diverse ideas related to...) * Innovate: (Propose novel or original solutions for...) * Analogize: (Find analogies or parallels for the following...) * Metaphorize: (Express the following concept using metaphors...) Potential Benefits of This Approach: * Efficiency: Shorter prompts could trigger complex reasoning. * Precision: Reduced ambiguity in the desired cognitive task. * Programmability: Easier to integrate specific thinking processes into automated workflows. * Interpretability (Potentially): If these directives map to identifiable internal modules, it could offer some insight into the AI's reasoning. Challenges and Considerations: * Training the Model: AI models would need to be specifically trained or fine-tuned to recognize and reliably execute these directives. This would likely require a large dataset annotated with these directives and the expected cognitive outputs. * Overfitting to Directives: There's a risk that the AI might become overly reliant on these specific keywords and fail to generalize to more natural language requests. * Defining the Scope: Carefully defining the precise meaning and expected output for each directive would be crucial to avoid ambiguity. * Human Learnability: Users would need to learn and understand this "AI programming language" of directives. Next Steps in Brainstorming: * Granularity: Should these directives be very specific (e.g., IdentifyCausalLinks:) or more general (e.g., AnalyzeRelationship:)? * Syntax: How would these directives be integrated into a prompt? As prefixes? As special tags? * Compositionality: Could directives be combined to trigger more complex sequences of thought? (e.g., Deconstruct: Synthesize:) * Meta-Directives: Could there be directives that modify the way other directives are executed (e.g., Thoroughly: Critique:)? This is a rich area for exploration. If AI models could reliably interpret and act upon such concise cognitive directives, it could significantly enhance our ability to guide their thinking and leverage their computational power for complex reasoning tasks.